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Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
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SOC 職業分類に基づく
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
Use when when you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e.
| name | abundance-normalization-and-summary-statistics |
| description | Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3443","edam_topics":["http://edamontology.org/topic_3172","http://edamontology.org/topic_0121"],"tools":["CoreMS","pandas","matplotlib"],"techniques":["mass-spectrometry"],"license_tier":"open"} |
| derived_from | [{"doi":"10.5281/zenodo.14009575","title":"corems"}] |
| evidence_spans | ["from corems.transient.input.brukerSolarix import ReadBrukerSolarix","**CoreMS** is a comprehensive mass spectrometry framework for software development and data analysis of small molecules analysis.","import pandas as pd"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_corems_cq","doi":"10.5281/zenodo.14009575","title":"corems"}],"dedup_kept_from":"coll_corems_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.5281/zenodo.14009575","all_source_dois":["10.5281/zenodo.14009575"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Computes and aggregates peak abundance metrics (total, percent, and relative abundance) across heteroatom classes or other mass spectrum groupings to enable normalized, class-level comparison and visualization of molecular composition. This skill transforms individual peak abundances into interpretable summary tables suitable for downstream analysis and publication.
Apply this skill after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series. Use it when individual peak-level data is too granular for interpretation and you require normalized aggregate metrics to report class abundance distributions or validate that all peaks are classified without loss.
Load a formula-assigned mass spectrum object from CoreMS containing peaks with both assigned molecular formulas and heteroatom class labels. Instantiate the HeteroatomsClassification factory with the mass spectrum object. Extract the abundance value (peak height or integrated area) for each detected peak. Group peaks by heteroatom class and compute: (1) peak count per class, (2) total abundance (sum of all peak abundances in that class), and (3) percent abundance (class total / spectrum-wide total × 100). Aggregate results into a pandas DataFrame with columns: heteroatom_class, peak_count, total_abundance, percent_abundance. Validate that the sum of percent_abundance across all classes equals 100% and that no peaks remain unclassified, confirming lossless partitioning.
from corems.mass_spectrum.calc import MassSpecCalc; from corems.mass_spectrum.factory import MassSpectrumFactory; ms = MassSpectrumFactory.from_file('ESI_NEG_SRFA.d'); hetero_class = ms.get_heteroatom_class_summary(); summary_df = hetero_class.to_dataframe(); summary_df.to_csv('heteroatom_abundance_summary.csv')